A Survey on Text Simplification

Abstract

Text Simplification (TS) aims to reduce the linguistic complexity of content to make it easier to understand. Research in TS has been of keen interest, especially as approaches to TS have shifted from manual, hand-crafted rules to automated simplification. This survey seeks to provide a comprehensive overview of TS, including a brief description of earlier approaches used, discussion of various aspects of simplification (lexical, semantic and syntactic), and latest techniques being utilized in the field. We note that the research in the field has clearly shifted towards utilizing deep learning techniques to perform TS, with a specific focus on developing solutions to combat the lack of data available for simplification. We also include a discussion of datasets and evaluations metrics commonly used, along with discussion of related fields within Natural Language Processing (NLP), like semantic similarity.

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References in corpus (6)

• For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia
• A Transition-Based Directed Acyclic Graph Parser for UCCA
• An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
• Vicinity-Driven Paragraph and Sentence Alignment for Comparable Corpora
• Sentence Simplification with Memory-Augmented Neural Networks
• Semantic Structural Evaluation for Text Simplification

Cited by in corpus (2)

• Summarization, Simplification, and Generation: The Case of Patents
• How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks